BACKGROUND AND PURPOSE: To determine the cost-effectiveness of specific interventions to prevent or treat acute stroke, it is necessary to know the costs of stroke according to patient characteristics and stroke subtype and etiology. However, very few such data are available and none from population-based studies. We determined the predictors of resource use and acute care costs of stroke using data from a population-based study. METHODS: Data were obtained from the Oxford Vascular study, a population-based cohort of all individuals in nine general practices in Oxfordshire, UK, which identified 346 patients with a first or recurrent stroke during April 1, 2002, to March 31, 2004. Univariate and multivariate analyses were performed to identify the main predictors of resource use and costs. RESULTS: Acute care costs ranged from 326 pounds sterling (lower decile) to 19,901 pounds sterling (upper decile). There were multiple important univariate interrelations of patient characteristics, stroke subtype, and stroke etiology with hospital admission, length of stay, and 30-day case-fatality. For example, patients with primary intracerebral hemorrhage were more likely to be admitted than patients with partial anterior circulation ischemic stroke and less likely to survive without disability, but length of stay was reduced as a result of high early case-fatality such that cost was substantially less. However, the majority of univariate predictors of resource use, cost, and outcome were confounded by initial stroke severity as measured by the National Institutes of Health Stroke Scale score, which accounted for approximately half of the predicted variance in cost. Cost increased approximately linearly up to an National Institutes of Health Stroke Scale score of 18 and then fell steeply at higher scores as a result of rising early case-fatality. CONCLUSIONS: Several patient and event-related characteristics explained the wide range of initial secondary care costs of acute stroke, but stroke severity was by far the most important independent predictor.
BACKGROUND AND PURPOSE: To determine the cost-effectiveness of specific interventions to prevent or treat acute stroke, it is necessary to know the costs of stroke according to patient characteristics and stroke subtype and etiology. However, very few such data are available and none from population-based studies. We determined the predictors of resource use and acute care costs of stroke using data from a population-based study. METHODS: Data were obtained from the Oxford Vascular study, a population-based cohort of all individuals in nine general practices in Oxfordshire, UK, which identified 346 patients with a first or recurrent stroke during April 1, 2002, to March 31, 2004. Univariate and multivariate analyses were performed to identify the main predictors of resource use and costs. RESULTS: Acute care costs ranged from 326 pounds sterling (lower decile) to 19,901 pounds sterling (upper decile). There were multiple important univariate interrelations of patient characteristics, stroke subtype, and stroke etiology with hospital admission, length of stay, and 30-day case-fatality. For example, patients with primary intracerebral hemorrhage were more likely to be admitted than patients with partial anterior circulation ischemic stroke and less likely to survive without disability, but length of stay was reduced as a result of high early case-fatality such that cost was substantially less. However, the majority of univariate predictors of resource use, cost, and outcome were confounded by initial stroke severity as measured by the National Institutes of Health Stroke Scale score, which accounted for approximately half of the predicted variance in cost. Cost increased approximately linearly up to an National Institutes of Health Stroke Scale score of 18 and then fell steeply at higher scores as a result of rising early case-fatality. CONCLUSIONS: Several patient and event-related characteristics explained the wide range of initial secondary care costs of acute stroke, but stroke severity was by far the most important independent predictor.
Authors: Darin B Zahuranec; Brisa N Sánchez; Devin L Brown; Jeffrey J Wing; Melinda A Smith; Nelda M Garcia; William J Meurer; Lewis B Morgenstern; Lynda D Lisabeth Journal: Cerebrovasc Dis Date: 2012-07-14 Impact factor: 2.762
Authors: Ramon Luengo-Fernandez; Alastair M Gray; Linda Bull; Sarah Welch; Fiona Cuthbertson; Peter M Rothwell Journal: Neurology Date: 2013-10-09 Impact factor: 9.910